Key tropical crops at risk from pollinator loss due to climate change and land use

Insect pollinator biodiversity is changing rapidly, with potential consequences for the provision of crop pollination. However, the role of land use–climate interactions in pollinator biodiversity changes, as well as consequent economic effects via changes in crop pollination, remains poorly understood. We present a global assessment of the interactive effects of climate change and land use on pollinator abundance and richness and predictions of the risk to crop pollination from the inferred changes. Using a dataset containing 2673 sites and 3080 insect pollinator species, we show that the interactive combination of agriculture and climate change is associated with large reductions in insect pollinators. As a result, it is expected that the tropics will experience the greatest risk to crop production from pollinator losses. Localized risk is highest and predicted to increase most rapidly, in regions of sub-Saharan Africa, northern South America, and Southeast Asia. Via pollinator loss alone, climate change and agricultural land use could be a risk to human well-being.


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Figs. S1 to S25 Tables S1 to S6 Figure S2.Response of pollinating insect total abundance to the standardised temperature anomaly on cropland, jack-knifed by dropping each taxonomic family in turn.Predictions were derived from a linear mixed-effects model of total abundance as a function of land use in interaction with the standardised temperature anomaly.The dark orange line represents the trend for all insect families combined, and each translucent line shows the modelled relationship with one family excluded.Note that abundance is plotted on a loge scale (although the labels are back-transformed).

Figure S3
. Response of pollinating insect total abundance to standardised temperature anomaly on cropland for a set of active season thresholds, calculated using two approaches: 1) Defining active months as any months in the baseline that have a temperature of at least 10°C, and then calculating the STA for only these months in the period 5 years previous to each PREDICTS site (i.e.'Baseline' calculation, orange dots); and 2) defining active months as any months 5 years previous to each PREDICTS site that have a temperature of at least 10°C, and then calculating the STA for only this set of months in the baseline, as in the approach used in ( 27)) (i.e.'PREDICTS site' calculation, black dots; note that this is for only one simulation, since these take much longer to run).Each point represents the percentage change in insect pollinator total abundance on cropland sites, between a standardised temperature anomaly of 0 and 1.The red dashed line represents change predicted on cropland with no active season adjustment, as in all main text figures.The standardised temperature anomaly is the change in mean of monthly mean daily temperatures between a baseline period (1901-1930) and 2004-2006, divided by the standard deviation across monthly mean daily temperatures across the 30-year baseline period.For any crop group in EarthStat represented by multiple estimated pollination dependencies, we took the dependence to be the mean across the individual crops.

Figure S12.
Proportional production risk at the level of each country in 2050 under the RCP 6.0 climate scenario, assuming a linear relationship between insect pollinator abundance loss and production loss for crops dependent on animal pollination.Here overall risk is the median of proportional production risk for all cells of that country, whilst change in risk is the difference in overall risk between the start and the end of the series.Point size here represents the total value of the pollination dependent production in that country as a proportion of GDP, calculated from the product of total pollination dependent production per annum according to (47) and ( 36) and the per tonne value of each crop (91).Colour represents the geographic region of each country, distinguishing between regions within a panel: Light blue, Africa; orange, Asia; black, Australia; green, Europe; dark blue, North America; yellow, Latin America; grey, the Caribbean.Table S1.The total number of cropland and primary vegetation sites for pollinating and likely non-pollinating insects in the PREDICTS database.

Land-use type
Pollinator status N (sites) Table S6.Model summary for a mixed-effects linear model predicting log(total abundance + 1) as a function of standardised temperature anomaly, mean annual temperature, land-use (cropland or primary vegetation), an interaction between mean annual temperature and landuse, and an interaction between standardised temperature anomaly and land-use, with the random intercepts study (SS) and block (SSB).

Figure S4 .
Figure S4.Sensitivity of projected changes in total production risk to dropping temperature

Figure S5 .
Figure S5.Sensitivity of projections of risk to crop production from extrapolating the underlying models beyond the range of sampled values of recent climate change.Shown are projected changes in total production risk under three RCP scenarios (8.5, 6.0, and 2.6), using average

Figure S6 .
Figure S6.Sensitivity of the projected index of relative production risk to variation in the quality

Figure S7 .
Figure S7.Sensitivity of projected crop production risk to the assumed relationship between

Figure S8 .
Figure S8.Spatial estimates of crop production that depends on animal pollination for the year 2000 (A), and the standardised temperature anomaly averaged for the years 2004 to 2006

Figure S9 .
Figure S9.Sensitivity of the spatial distribution of proportional production risk to variation in the assumed relationship between po llinator

Figure S10 .
Figure S10.Variation in proportional production risk according to the assumed relationship between pollinator abundance and production risk.Projections are for the RCP 6.0 climate

Figure S11 .
Figure S11.Total production dependent on animal pollination for the 20 crops with the highest values.Total production values are for the year 2000, taken from EarthStat (47).Animal-

Figure S13 .
Figure S13.Proportional production risk for the 20 crops with the highest total pollination dependent production globally (see Figure S11 for the top 20 crops) in 2050 under the RCP

Figure S14 .
Figure S14.Spatial distribution of the sites in the PREDICTS database for both pollinating (A and C) and non-pollinating insects (B and D).In panels (A) and (B), colours indicate the

Figure S15 .
Figure S15.Response of species richness of pollinating insects (estimated using the Chao

Figure S16 .
Figure S16.Projected change globally in crop production estimated to be at risk in 2050 under the RCP 6.0 climate scenario, assuming a linear relationship between species richness of

Figure S17 .
Figure S17.Projected change globally in crop production estimated to be at risk in 2050 under the RCP 6.0 climate scenario, assuming a linear relationship between bee abundance loss

Figure S18 .
Figure S18.Frequency of insect pollinator species in the PREDICTS database for each

Figure S19 .
Figure S19.Frequency of insect pollinator species in the PREDICTS database for each

Figure S20 .
Figure S20.Sampled total abundance (+1) of insect pollinators in the PREDICTS database

Figure S21 .
Figure S21.The distribution of sampling period for all insect pollinator records in PREDICTS.

Figure S22 .
Figure S22.Response of pollinating insect total abundance to the interactive effect of

Figure S23 .
Figure S23.The frequency of records for each sampling method used to collect insect pollinator data in the PREDICTS database (total number of records equals 112555).

Figure S24 .
Figure S24.Mean annual temperature (°C) at the year of sampling plotted against the standardised temperature anomaly, for each site in the PREDICTS database at which some

Figure S25 .
Figure S25.Simple directed acyclic graphs for the effect of mean annual temperature and standardised temperature anomaly on insect pollinator total abundance, on either primary

Table S2 .
The total number of species in the PREDICTS database for pollinating and likely non-pollinating insects, sampled in either cropland or primary vegetation.

Table S3 .
AIC and R 2 values for linear mixed-effects models with different random-effects structures fitting loge total abundance as a function of land-use type (cropland and primary vegetation), standardised climate anomaly, and their interaction, for both pollinating and likely non-pollinating insect species.Random-effects structures considered were either study identity, or study identity and spatial block nested within study.

Table S4 .
Proportional dependence on animal pollination for the crops/crop groups mapped in EarthStat (47).For each crop group, we show the average and standard error of proportional

Table S5 .
Model summary for a mixed-effects linear model predicting log(total abundance + 1) as a function of standardised temperature anomaly, land-use (cropland or primary vegetation), and an interaction between land-use and standardised temperature anomaly, with the random intercepts study (SS) and block (SSB).